In addition to slot description, other zero-shot approaches explore the use of slot value examples [1]}, [2]}. shah-etal-2019-robust showing that a combination of a small number of slot values examples with a slot description performs better than [3]}, [4]} on the SNIPS dataset. Zero-shot models are typically trained on a per-slot basis (Figure REF  Right), meaning that if we have \(N\)  slots, then the model will output \(N\)  predictions, therefore, a merging mechanism is needed in case there are prediction overlaps. In order to alleviate the problem of having multiple predictions, DBLP:conf/acl/LiuWXF20 propose a coarse-to-fine approach, in which the model learns the slot entity pattern (coarsely) to identify a particular token is an entity or not. After that, the model performs a single prediction of the slot type (fine) based on the similarity between the feature representation and the slot description.
